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Sources of household income

The National Income and Product Accounts (NIPA) provide a wealth of economic insights by separating data into different classes, one of them being households. FRED makes it especially easy to examine the sources of household income by providing release tables. Here, we used the table for personal income to create the graph above.

Probably to no one’s surprise, wages and salaries make up the largest share of household income. But this share has been steadily shrinking, from 63% in 1947 to 49% today. Proprietors’ income (the income of the self-employed) has also shrunk, from 19% down to 9%.

Obviously, other shares must be growing. Capital income has grown from 8% to 14%. Supplements to wages and salaries (benefits for health, retirement, vacation, etc.) have grown from 5% to 12%. And transfers from the government have grown the most, from 5% to 17%.

How this graph was created: From the Personal Income release table, select these five series and click “Add to Graph.” In the “Edit Graph” tab, go to the “Format” tab to select graph type “Area” and stacking type “Percent.” FYI: FRED lets you change the order of the series as you wish. [NOTE: This stacked area graph displays each of the series as a percent of the total of all five series shown here. The original units of the series are billions of dollars.]

Suggested by Christian Zimmermann.

View on FRED, series used in this post: A038RC1Q027SBEA, A577RC1Q027SBEA, PROPINC, W210RC1Q027SBEA, WASCUR

Government transfers to households: From 1947 to 1966 to now

One thing governments do is redistribute wealth from some citizens to others. Some of these transfers are explicitly captured in the system of national accounts, and the graph includes the five largest categories. It’s striking how the composition of these transfers has changed. The data start in 1947, so it’s no surprise the support of WWII veterans took the lion’s share for the first years. The number of veterans eligible for benefits declined as social security progressively expanded. In 1966, Medicaid and Medicare were introduced, and we see their shares slowly increasing. Today, social security benefits claim the largest share of government transfers: 40%. Medicare claims 29%,  Medicaid claims 25%, unemployment insurance benefits claim 1%, and veterans’ benefits claim 4%.

How this graph was created: These national accounts data can be found in the personal income release table. Check these five series and click on “Add to Graph.” From the “Edit Graph” panel, go to the “Format” tab to choose “Area” as the type of graph and “Percent” as the type of stacking for the graph. [NOTE: This stacked area graph displays each of the series as a percent of the total of all five series shown here. As the y-axis label indicates, the original units of the series are billions of dollars.]

Suggested by Christian Zimmermann.

View on FRED, series used in this post: W729RC1Q027SBEA, W823RC1Q027SBEA, W824RC1Q027SBEA, W825RC1Q027SBEA, W826RC1Q027SBEA

Friction in oil markets

The graph shows the price of a barrel of oil. Two types, to be exact: The blue line shows West Texas Intermediate (WTI) quality oil at delivery in Cushing, Oklahoma, a significant pipeline hub. The red line shows oil from the North Sea, referred to as Brent Crude. The two lines are typically very close to each other, with Brent being about $3 cheaper because of its slightly different characteristics and transportation costs. But things change for the years 2011 to 2014: WTI is much cheaper—up to $26 cheaper. What happened? Many factors may have contributed to this phenomenon, the most likely being the increased extraction of tar sands in Alberta, Canada, and a boom in oil extraction through fracking in the interior U.S. This glut overwhelmed the transport infrastructure and made it difficult to move all this oil to destination. Once more pipelines came online and the railroad transport toward the East Coast expanded, the price differential returned to normal, with relatively frictionless arbitrage between the various oil types and thus similar prices. This means that the different blends can be traded on the market as close substitutes while being easily accessible, and this makes their prices converge toward each other.

How this graph was created: Search for “crude oil price,” select the two series, and click on “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: DCOILBRENTEU, DCOILWTICO

Wages with benefits

Nominal wages generally increase, but the picture is mixed for real wages. The green line in the top graph shows real wage growth, which is negative a fair amount of the time. Bursts in inflation can counteract the usually small increases in nominal wages. In fact, the strong growth of real wages at the end of the past recession is mostly due to a short episode of deflation.

But wages aren’t the whole story. A job usually also involves other types of compensation, such as the employer’s contribution to retirement pensions, health and life insurance, paid vacation and other leave, and any taxes the employer pays on these benefits. These benefits are now a substantial part of the cost of an employee, and they appear to be growing. The top graph shows that labor compensation growth is frequently higher than real wage growth. We can make this point more clearly by using index values: In the bottom graph, we set both series at 100 in 1970 and let them run. Real compensation growth is significantly higher: the 60% increase looks much better than the 3% increase for real wages.

How these graphs were created: Search for “real compensation” and click on the series shown. In the “Edit Graph” panel, add a new line by searching for “hourly earnings.” Then, within the same panel, add a series by searching for “CPI.” Apply formula a/b to the second line to make earnings real. For the first graph, set units for both lines to “Percent Change from Year Ago”; for the second line, you do this at the bottom of the panel. For the second graph, the selected units are “Index (scale value to 100 for chosen period)”; set the date as 1970-01-01.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: AHETPI, CPIAUCSL, RCPHBS

Shaking things up in China

During President Obama’s recent visit to China, even getting off the plane involved political upheaval: The New York Times described the mood as “tense” when disagreements between Chinese and U.S. officials compelled the president to use an alternative stairway to deplane Air Force One.

Chinese economic policy has also been tense for some time now, independent of their ability or willingness to accommodate a foreign 747. The graph above plots the Economic Policy Uncertainty Index from Baker, Bloom, and Davis for the U.S. and China. This index scans news articles about a country and records the frequency of phrases that connote economic policy uncertainty. When it’s high, the press is using language that suggests the government could change its regulations, spending, and/or taxes in the near future. As the authors point out, this uncertainty complicates planning and can adversely affect investment. It can also, however, reflect economic conditions themselves; as the economy sours, the political response is often uncertain as sides debate how best to respond.

Until recently, China and the U.S. tracked each other quite well, and such a connection might reflect common economic conditions in the two countries. But China did not share the U.S. experience during the 2001 recession; it shared only the rise in uncertainty. The bottom graph adds GDP growth to the mix, and the “pattern” we see has almost no pattern to it. GDP is slowing in China, but policy uncertainty seems to be hyperactive. Chinese GDP declined during the Great Recession, and since then the decline seems to have been smooth and slight. Policy language, however, has vacillated quite wildly. Perhaps President Obama should feel lucky his stairway remained in place as he descended.

How these graphs were created: Top graph: Search for “Economic Policy Uncertainty Index” and select the U.S. and China among the countries given. Convert both to a quarterly frequency for two reasons: The frequency of U.S. GDP is also quarterly, and the monthly swings in the Chinese index are so great they make it difficult to visualize the U.S. index. Bottom graph: Add two lines to the top graph: seasonally adjusted real U.S. GDP (converting it to a percentage change) and constant China GDP, which should give the U.S. dollar-denominated GDP (again, converting it to a percentage change). For both these new lines, go to the “Format” tab in the “Edit Graph” section and move the units to the right vertical axis. Note: FRED doesn’t have updated Chinese real GDP after 2014, but the latest figure from the National Bureau of Statistics in China puts growth at 6.7% in 2016:Q2, slightly lower than the 7.3% recorded in 2014, as shown in the graph.

Suggested by David Wiczer.

View on FRED, series used in this post: CHIEPUINDXM, GDPC1, RGDPNACNA666NRUG, USEPUINDXM

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